A system on chip for melanoma detection using FPGA-based SVM classifier

TL;DR

This paper presents an FPGA-based system on chip implementing an SVM classifier for melanoma detection, achieving high accuracy, low power consumption, and efficient hardware acceleration suitable for embedded healthcare devices.

Contribution

It introduces a hardware/software co-design on FPGA for melanoma detection, optimizing SVM implementation for embedded systems with high accuracy and low resource usage.

Findings

Classification accuracy of 97.9%

Hardware acceleration rate of 21x

Power consumption of 1.69W

Abstract

Support Vector Machine (SVM) is a robust machine learning model that shows high accuracy with different classification problems, and is widely used for various embedded applications. However , implementation of embedded SVM classifiers is challenging, due to the inherent complicated computations required. This motivates implementing the SVM on hardware platforms for achieving high performance computing at low cost and power consumption. Melanoma is the most aggressive form of skin cancer that increases the mortality rate. We aim to develop an optimized embedded SVM classifier dedicated for a low-cost handheld device for early detection of melanoma at the primary healthcare. In this paper, we propose a hardware/software co-design for implementing the SVM classifier onto FPGA to realize melanoma detection on a chip. The implemented SVM on a recent hybrid FPGA (Zynq) platform utilizing the modern UltraFast High-Level Synthesis design methodology achieves efficient melanoma classification on chip. The hardware implementation results demonstrate classification accuracy of 97.9%, and a significant hardware acceleration rate of 21 with only 3% resources utilization and 1.69W for power consumption. These results show that the implemented system on chip meets crucial embedded system constraints of high performance and low resources utilization, power consumption, and cost, while achieving efficient classification with high classification accuracy.